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Single-Domain Amnestic Mild Cognitive Impairment Identified by Cluster Analysis Predicts Alzheimer's Disease in the European Prospective DESCRIPA Study

Damian, Marinella; Hausner, Lucrezia; Jekel, Katrin; Richter, Melany; Froelich, Lutz; Almkvist, Ove; Boada, Merce; Bullock, Roger; De Deyn, Peter Paul and Frisoni, Giovanni B., et al. (2013) In Dementia and Geriatric Cognitive Disorders 36(1-2). p.1-19
Abstract
Background/Aims: To identify prodromal Alzheimer's disease (AD) subjects using a data-driven approach to determine cognitive profiles in mild cognitive impairment (MCI). Methods: A total of 881 MCI subjects were recruited from 20 memory clinics and followed for up to 5 years. Outcome measures included cognitive variables, conversion to AD, and biomarkers (e. g. CSF, and MRI markers). Two hierarchical cluster analyses (HCA) were performed to identify clusters of subjects with distinct cognitive profiles. The first HCA included all subjects with complete cognitive data, whereas the second one selected subjects with very mild MCI (MMSE >= 28). ANOVAs and ANCOVAs were computed to examine whether the clusters differed with regard to... (More)
Background/Aims: To identify prodromal Alzheimer's disease (AD) subjects using a data-driven approach to determine cognitive profiles in mild cognitive impairment (MCI). Methods: A total of 881 MCI subjects were recruited from 20 memory clinics and followed for up to 5 years. Outcome measures included cognitive variables, conversion to AD, and biomarkers (e. g. CSF, and MRI markers). Two hierarchical cluster analyses (HCA) were performed to identify clusters of subjects with distinct cognitive profiles. The first HCA included all subjects with complete cognitive data, whereas the second one selected subjects with very mild MCI (MMSE >= 28). ANOVAs and ANCOVAs were computed to examine whether the clusters differed with regard to conversion to AD, and to AD-specific biomarkers. Results: The HCAs identified 4-cluster solutions that best reflected the sample structure. One cluster (aMCIsingle) had a significantly higher conversion rate (19%), compared to subjective cognitive impairment (SCI, p < 0.0001), and non-amnestic MCI (naMCI, p = 0.012). This cluster was the only one showing a significantly different biomarker profile (A beta(42), t-tau, APOE epsilon 4, and medial temporal atrophy), compared to SCI or naMCI. Conclusion: In subjects with mild MCI, the single-domain amnestic MCI profile was associated with the highest risk of conversion, even if memory impairment did not necessarily cross specific cut-off points. A cognitive profile characterized by isolated memory deficits may be sufficient to warrant applying prevention strategies in MCI, whether or not memory performance lies below specific z-scores. This is supported by our preliminary biomarker analyses. However, further analyses with bigger samples are needed to corroborate these findings. Copyright (C) 2013 S. Karger AG, Basel (Less)
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@article{0dda84f6-87ad-42d4-bdab-a7f48bbad55f,
  abstract     = {Background/Aims: To identify prodromal Alzheimer's disease (AD) subjects using a data-driven approach to determine cognitive profiles in mild cognitive impairment (MCI). Methods: A total of 881 MCI subjects were recruited from 20 memory clinics and followed for up to 5 years. Outcome measures included cognitive variables, conversion to AD, and biomarkers (e. g. CSF, and MRI markers). Two hierarchical cluster analyses (HCA) were performed to identify clusters of subjects with distinct cognitive profiles. The first HCA included all subjects with complete cognitive data, whereas the second one selected subjects with very mild MCI (MMSE &gt;= 28). ANOVAs and ANCOVAs were computed to examine whether the clusters differed with regard to conversion to AD, and to AD-specific biomarkers. Results: The HCAs identified 4-cluster solutions that best reflected the sample structure. One cluster (aMCIsingle) had a significantly higher conversion rate (19%), compared to subjective cognitive impairment (SCI, p &lt; 0.0001), and non-amnestic MCI (naMCI, p = 0.012). This cluster was the only one showing a significantly different biomarker profile (A beta(42), t-tau, APOE epsilon 4, and medial temporal atrophy), compared to SCI or naMCI. Conclusion: In subjects with mild MCI, the single-domain amnestic MCI profile was associated with the highest risk of conversion, even if memory impairment did not necessarily cross specific cut-off points. A cognitive profile characterized by isolated memory deficits may be sufficient to warrant applying prevention strategies in MCI, whether or not memory performance lies below specific z-scores. This is supported by our preliminary biomarker analyses. However, further analyses with bigger samples are needed to corroborate these findings. Copyright (C) 2013 S. Karger AG, Basel},
  author       = {Damian, Marinella and Hausner, Lucrezia and Jekel, Katrin and Richter, Melany and Froelich, Lutz and Almkvist, Ove and Boada, Merce and Bullock, Roger and De Deyn, Peter Paul and Frisoni, Giovanni B. and Hampel, Harald and Jones, Roy W. and Kehoe, Patrick and Lenoir, Hermine and Minthon, Lennart and Rikkert, Marcel G. M. Olde and Rodriguez, Guido and Scheltens, Philip and Soininen, Hilkka and Spiru, Luiza and Touchon, Jacques and Tsolaki, Magda and Vellas, Bruno and Verhey, Frans R. J. and Winblad, Bengt and Wahlund, Lars-Olof and Wilcock, Gordon and Visser, Pieter Jelle},
  issn         = {1420-8008},
  keyword      = {Mild cognitive impairment,Alzheimer's disease,Mild cognitive,impairment subtypes,Conversion to Alzheimer's disease},
  language     = {eng},
  number       = {1-2},
  pages        = {1--19},
  publisher    = {Karger},
  series       = {Dementia and Geriatric Cognitive Disorders},
  title        = {Single-Domain Amnestic Mild Cognitive Impairment Identified by Cluster Analysis Predicts Alzheimer's Disease in the European Prospective DESCRIPA Study},
  url          = {http://dx.doi.org/10.1159/000348354},
  volume       = {36},
  year         = {2013},
}